This presentation is part of CLL Global Research Foundation’s first-ever Patient-Focused research symposium, featuring CLL Global–funded researchers sharing insights from their latest studies and clinical trials—showcasing how their work is directly improving outcomes for patients with chronic lymphocytic leukemia (CLL).
Expert Presenter:
Natalia Timofeeva, MD
Postdoctoral Fellow, Department of Translational Molecular Pathology
The University of Texas MD Anderson Cancer Center
Download the slide deck.
Transcript:
Dr. Natalia Timofeeva: Good afternoon, everybody. I would like to say that I’m really proud to be here as a young researcher and present my data and my research to the CLL community, to the patients and their families, because for me, it feels that I do something real, I will have the consequences of my research and my hard work. And I also would like to thank the CLL Global for giving me the opportunity to start my post-doctoral fellowship at MD Anderson and begin my scientific career. So, I’m really happy that today, we are able to talk a little bit about CLL biology and the origin, so I don’t need to explain a lot of details on the background, but I also want to dig a little bit deeper into the concept of targeted therapy.
Dr. Gandhi already explained about two major drug classes in the CLL. But I want to emphasize why we stick to Bruton’s tyrosine kinase. Why we stick to B-cell receptor pathway and why ibrutinib (Imbruvica) and newer generation BTK inhibitors are so important to us. And also how this treatment causes the disease progression and mutation development, and how can we fight back and find the better options for the CLL patients. So, here I would like to show how the targeted therapy works, and I think it will be easier to explain how the B-cell receptor works for the normal B cells and for the CLL B cells. It’s basically part of the signal transduction between the surface of the cell to the nucleus, the control center.
And in normal B cells, we need that in order to respond to external stimuli from the induction, for example. So, B cells getting activated and respond to the trigger. But, in cancer cells, this signaling becomes activated on its own without any external signals. And in this case, CLL cells proliferate, multiply, and survive without any restrictions. In this case, we will get many CLL cells proliferating in the blood, in the bone marrow, and in the lymph node, which causes consequences. And in this B-cell receptor, we have many molecules that are responsible for a signal transduction down to the nucleus. And one of the molecules is the BTK. And BTK is a critical molecule, and if we inhibit this with a BTK inhibitor such as ibrutinib, we just basically shut down the downstream signaling to the nucleus, so cells cannot divide, they cannot multiply, they are not getting activated, and to not migrate. Sounds good, right?
But why do we have CLL as an incurable disease, basically? It’s not that simple because patients usually go through the cycles of remission and disease progression, and as Dr. Jain mentioned, we have three major classes of the drugs, the BTK inhibitors, the BCL-2 inhibitors, and the anti-CD20 monoclonal antibodies that we mix and match in order to get the therapeutic response. But over time, patients still develop resistance, and we need to change the therapy. We need to either add the new drug, or we need to switch to the different class and different generation of the drug in order to overcome this resistance. And it happens multiple times, and sometimes after a few lines of therapy, we end up with the question, “What to do next?” And what strategy we need to apply in order to get another remission and get another time for the patient.
Why the drugs that we use do not work? And I would really like to imagine this as a race between the drug development and the drug resistance. So, basically, when we find a target, for example, in the BTK, if we imagine this as a chain of amino acids, there is a BTK kinase domain that is responsible for the activation of the BTK. And it has the position 481, the cysteine amino acid. That binds, that serves as a binding site for ibrutinib.
So, ibrutinib docks to this cysteine at 481 position and shuts down the molecule. Looks like we won this round, right? But in case if for some reason the cysteine amino acid change to serine, ibrutinib was not able to bind anymore to the molecule, and it becomes ineffective. So, BTK is still activated, disease is still active, and the drug is ineffective. Okay, we’ll take that, and re-invented ibrutinib, non-covalent inhibitor. That’s not meeting the specific bonding sites; it helps multiple. It’s not that strong, the binding is not that strong as for ibrutinib, but with ibrutinib is really effective, so it can bind multiple places, and this kind is the main and still inhibit its activity.
But we still have some other mutations. So, basically, CLL decides that it doesn’t need BTK anymore; it can just shut it down with different mutations. For example, L52AW, even though ibrutinib bond to this molecule, it becomes ineffective. So, in this case, we need to find different strategies. How to alter form, how to defeat the CLL in this race. And this is why we need such thing as the ex-vivo drug profiling. Ex-vivo means outside the living. So we basically take the peripheral blood from the patient, and again, I want to emphasize, we are really grateful for the patients who agree to donate the research samples because we are looking forward to it every time, because it’s some new material to work with, and the more data, the more knowledge we get from the samples about the CLL.
And we separate this blood into different fractions and isolate specifically the CLL cells. Then we plate them in the culture with the different drugs of interest. It might be not only the drugs that we use in the clinic, it might be that some potential drugs that are not being developed for the clinic use yet, but we want to study the mechanisms. Then we assess the specific protein expression of the cell viability. And based on the information that we receive, we run biostatistical and bioinformatic analysis and try to compile all this results in some story. So, I want to show a little bit of our real data that we have. For example, in one year, we profiled 22 patients that had the disease relapse, and those patients had different types of mutations, BTK, BCL-2 mutations, or some mutations that were not caused by the treatment and were initially with the patients.
But all of this, it impacts the clinical outcome for the patients. And we tested three main classes of the drugs and different generations of these drugs, so we mix and match in combination some single-treatments and we got different responses. So, if you can see on the heat map, there are some green and red squares it means that high or low viability. High or low cell death. And you can see that different patients on the bottom there are ID numbers. They respond differently, but why? Some patients are really sensitive to the drugs that we have, and some patients are not. And that’s why we realize that genetic background really matters. So, we have a lot of different mutations and their combinations. For example, double refractory patients who have both BTK and BCL-2 mutations that are highly resistant to all the treatments that we introduced. So, in this case, we need to think about more personalized treatments, more strategies, maybe new targets for these patients, because the standard of care doesn’t work for these patients.
So, one of the strategies, the combination strategy, and Dr. Ghandi brilliantly showed us how two drugs can work together, better than in monotherapy. And we tested different combinations of the drugs with different mutations of BTK and BCL-2 and found out that combination treatments, for example, works better for more aggressive mutations. Or we test the different BCL-2 inhibitors, same class but just different generations, and we found that newer generations of BCL-2 inhibitors, they kill more cells in the lower concentration that the first generation, venetoclax (Venclexta), for example, would. So in this case, the drug sensitivity profile helps us first to understand what are the resistance mechanisms and why the drugs are not working.
And find a new target because we cannot use some drugs in the clinic, but we can test them in vitro, in the lab settings. Then we can test new combinations. Again, so we do not use this in clinic right now, but we can do it in the lab and see how it works. Maybe we will get the good synergistic effect. And based on the information that we have that we gathered, different patterns of sensitivity related to different mutations, we can use this information for the prediction model using AI or machine learning to understand how these patterns of resistance impact the clinical outcome.
This all together helps us to understand what would be better options for patients with certain mutations, with certain medical history, with a certain background, with a certain drug response pattern during our ex-vivo research. So, in this case, I would like to emphasize that not all CLL patients are the same; they’re all unique. They’re all require a tailored approach. And I guess with new technologies, with new AI technologies, with new lab techniques, we can progress, and we can find the better personalized treatment for these patients. And I would like to thank my group and my mentor, Dr. Ghandi, the CLL Global for providing the opportunity to run my research, the clinical team, and, of course, the patients and their families for having us at this beautiful evening. Thank you.